System and method for property condition analysis

A method, comprising:

• determining, by a computing system, a set of measurements depicting a property, the set of measurements including imagery data corresponding to a time window;
• for each measurement, extracting, by the computing system, a feature value vector from the measurement using a feature extractor, the feature value vector being indicative of a visual feature of the property, wherein each feature value vector is extracted by:
• determining a semantic segmentation mask based on the measurement using a semantic segmentation model;
• segmenting property feature pixels from the measurement based on the semantic segmentation mask;
• identifying property feature pixels based on the semantic segmentation mask;
• and
• extracting the feature value vector, based only on the identified property feature pixels, using the feature extractor;
• for each feature value vector:
• determining, by the computing system, a scaling factor indicating a weight of the visual feature in determining a condition of the property;
• and
• applying, by the computing system, the scaling factor to obtain a scaled feature value vector;
• determining, by the computing system, a set of attribute values based on the scaled feature value vector of each measurement, using a machine learning model;
• determining, by the computing system, a condition score for each end of the time window based on the set of attribute values, using a condition scoring model;
• determining, by the computing system, a property analysis indicating a change in a property condition based on the condition score;
• and
• increasing or decreasing an insurance premium based on the change in the property condition and the condition score,
• wherein the attribute values comprise semantic attribute values including at least one of discoloration, tarp presence, material degradation, missing material, sealing, or defects.